RFOP: Rethinking Fusion and Orthogonal Projection for Face-Voice Association

Fuente: arXiv
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Autores principales: Hannan, Abdul, Malik, Furqan, Jabbar, Hina, Sadiq, Syed Suleman, Noman, Mubashir
Formato: Preprint
Publicado: 2025
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author Hannan, Abdul
Malik, Furqan
Jabbar, Hina
Sadiq, Syed Suleman
Noman, Mubashir
author_facet Hannan, Abdul
Malik, Furqan
Jabbar, Hina
Sadiq, Syed Suleman
Noman, Mubashir
contents Face-voice association in multilingual environment challenge 2026 aims to investigate the face-voice association task in multilingual scenario. The challenge introduces English-German face-voice pairs to be utilized in the evaluation phase. To this end, we revisit the fusion and orthogonal projection for face-voice association by effectively focusing on the relevant semantic information within the two modalities. Our method performs favorably on the English-German data split and ranked 3rd in the FAME 2026 challenge by achieving the EER of 33.1.
format Preprint
id arxiv_https___arxiv_org_abs_2512_02860
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle RFOP: Rethinking Fusion and Orthogonal Projection for Face-Voice Association
Hannan, Abdul
Malik, Furqan
Jabbar, Hina
Sadiq, Syed Suleman
Noman, Mubashir
Computer Vision and Pattern Recognition
Face-voice association in multilingual environment challenge 2026 aims to investigate the face-voice association task in multilingual scenario. The challenge introduces English-German face-voice pairs to be utilized in the evaluation phase. To this end, we revisit the fusion and orthogonal projection for face-voice association by effectively focusing on the relevant semantic information within the two modalities. Our method performs favorably on the English-German data split and ranked 3rd in the FAME 2026 challenge by achieving the EER of 33.1.
title RFOP: Rethinking Fusion and Orthogonal Projection for Face-Voice Association
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2512.02860